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Calibration of the global physical activity questionnaire to Accelerometry measured physical activity and sedentary behavior

Overview of attention for article published in BMC Public Health, March 2018
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  • Above-average Attention Score compared to outputs of the same age (62nd percentile)
  • Average Attention Score compared to outputs of the same age and source

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Title
Calibration of the global physical activity questionnaire to Accelerometry measured physical activity and sedentary behavior
Published in
BMC Public Health, March 2018
DOI 10.1186/s12889-018-5310-3
Pubmed ID
Authors

Kristen M. Metcalf, Barbara I. Baquero, Mayra L. Coronado Garcia, Shelby L. Francis, Kathleen F. Janz, Helena H. Laroche, Daniel K. Sewell

Abstract

Self-report questionnaires are a valuable method of physical activity measurement in public health research; however, accuracy is often lacking. The purpose of this study is to improve the validity of the Global Physical Activity Questionnaire by calibrating it to 7 days of accelerometer measured physical activity and sedentary behavior. Participants (n = 108) wore an ActiGraph GT9X Link on their non-dominant wrist for 7 days. Following the accelerometer wear period, participants completed a telephone Global Physical Activity Questionnaire with a research assistant. Data were split into training and testing samples, and multivariable linear regression models built using functions of the GPAQ self-report data to predict ActiGraph measured physical activity and sedentary behavior. Models were evaluated with the testing sample and an independent validation sample (n = 120) using Mean Squared Prediction Errors. The prediction models utilized sedentary behavior, and moderate- and vigorous-intensity physical activity self-reported scores from the questionnaire, and participant age. Transformations of each variable, as well as break point analysis were considered. Prediction errors were reduced by 77.7-80.6% for sedentary behavior and 61.3-98.6% for physical activity by using the multivariable linear regression models over raw questionnaire scores. This research demonstrates the utility of calibrating self-report questionnaire data to objective measures to improve estimates of physical activity and sedentary behavior. It provides an understanding of the divide between objective and subjective measures, and provides a means to utilize the two methods as a unified measure.

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The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 82 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 17%
Student > Master 13 16%
Student > Bachelor 9 11%
Researcher 7 9%
Student > Doctoral Student 4 5%
Other 12 15%
Unknown 23 28%
Readers by discipline Count As %
Medicine and Dentistry 15 18%
Sports and Recreations 13 16%
Nursing and Health Professions 6 7%
Psychology 4 5%
Neuroscience 3 4%
Other 10 12%
Unknown 31 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 January 2023.
All research outputs
#7,105,531
of 23,524,722 outputs
Outputs from BMC Public Health
#7,374
of 15,257 outputs
Outputs of similar age
#121,991
of 331,271 outputs
Outputs of similar age from BMC Public Health
#219
of 322 outputs
Altmetric has tracked 23,524,722 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 15,257 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.1. This one has gotten more attention than average, scoring higher than 51% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 331,271 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.
We're also able to compare this research output to 322 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.